Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation

Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation

Blog Article

Aiming at the problem that the Web service quality of service (QoS) estimation methods based on the non-negative gymnastics wall decals latent factorization of tensor model (NLFT) depend heavily on non-negative initial random data and specially designed non-negative training schemes, which lead to low compatibility and scalability, an accelerated unconstrained latent factorization of tensor (AULFT) model was proposed.The proposed model consisted of three main parts.The non-negative constraints from decision parameters were transferred to output latent factors and they were connected through the single-element-dependent mapping function.A momentum-incorporated stochastic gradient descent (MSGD) algorithm was used to effectively improve the convergence rate and estimation accuracy of the proposed AULFT model.

The detailed algorithm and result analysis of the proposed AULFT model were presented.The empirical study on two dynamic QoS datasets in real industrial applications demonstrates that the proposed AULFT model has higher computational efficiency and estimation accuracy than the lycogel state-of-the-art QoS estimation models.